I am pretty annoyed by my problem here and couldn't really find anything useful.
The problem is, I have huge images (we're talking about 4000*5000 pixels) where I detect objects and add bounding boxes as well as some thin lines. So far, so good. Now I want to accurately analyze many of those images in parallel, where the MATLAB figure window is just too slow. Thus, I want to save those images. What happens then, is that the thin lines in the MATLAB figure window become thicker making the graphics file more or less useless.
Here's the code:
img = imread('blabla.jpg');
polyfig = figure, imagesc(img);
axis off
axis image
hold on
for i=1:nl
line([xmin xmax],[lines(1,i) lines(1,i)],'Color','r', 'LineWidth', 1);
end
This results in the nice figure, where, when zooming in, the lines are very very thin as I want them to be. Now I want to save those images:
set(polyfig,'PaperUnits','centimeters','PaperPosition',[0 0 100 100])
outfile = sprintf('/folders/result_poly_%s.eps', img_name(1:end-4));
print('-deps', outfile, '-r400');
I tried tons of different options when saving, but never getting the results I want to. Here are two examples:
http://imgur.com/kRkPLNY
http://imgur.com/FCP41Hk
I really don't know where I could change something to get what I want to get. Maybe you have a hint for me? I'd greatly appreciate that!
You can capture the image from the figure with :
h = figure(1); imshow(Img);
saveas(h,'SavePath');
Related
I have roughly 160 images for an experiment. Some of the images, however, have clearly different levels of brightness and contrast compared to others. For instance, I have something like the two pictures below:
I would like to equalize the two pictures in terms of brightness and contrast (probably find some level in the middle and not equate one image to another - though this could be okay if that makes things easier). Would anyone have any suggestions as to how to go about this? I'm not really familiar with image analysis in Matlab so please bear with my follow-up questions should they arise. There is a question for Equalizing luminance, brightness and contrast for a set of images already on here but the code doesn't make much sense to me (due to my lack of experience working with images in Matlab).
Currently, I use Gimp to manipulate images but it's time consuming with 160 images and also just going with subjective eye judgment isn't very reliable. Thank you!
You can use histeq to perform histogram specification where the algorithm will try its best to make the target image match the distribution of intensities / histogram of a source image. This is also called histogram matching and you can read up about it on my previous answer.
In effect, the distribution of intensities between the two images should hopefully be the same. If you want to take advantage of this using histeq, you can specify an additional parameter that specifies the target histogram. Therefore, the input image would try and match itself to the target histogram. Something like this would work assuming you have the images stored in im1 and im2:
out = histeq(im1, imhist(im2));
However, imhistmatch is the more better version to use. It's almost the same way you'd call histeq except you don't have to manually compute the histogram. You just specify the actual image to match itself:
out = imhistmatch(im1, im2);
Here's a running example using your two images. Note that I'll opt to use imhistmatch instead. I read in the two images directly from StackOverflow, I perform a histogram matching so that the first image matches in intensity distribution with the second image and we show this result all in one window.
im1 = imread('http://i.stack.imgur.com/oaopV.png');
im2 = imread('http://i.stack.imgur.com/4fQPq.png');
out = imhistmatch(im1, im2);
figure;
subplot(1,3,1);
imshow(im1);
subplot(1,3,2);
imshow(im2);
subplot(1,3,3);
imshow(out);
This is what I get:
Note that the first image now more or less matches in distribution with the second image.
We can also flip it around and make the first image the source and we can try and match the second image to the first image. Just flip the two parameters with imhistmatch:
out = imhistmatch(im2, im1);
Repeating the above code to display the figure, I get this:
That looks a little more interesting. We can definitely see the shape of the second image's eyes, and some of the facial features are more pronounced.
As such, what you can finally do in the end is choose a good representative image that has the best brightness and contrast, then loop over each of the other images and call imhistmatch each time using this source image as the reference so that the other images will try and match their distribution of intensities to this source image. I can't really write code for this because I don't know how you are storing these images in MATLAB. If you share some of that code, I'd love to write more.
I have a really long picture where I use imfindcircles on. But I need to check if the right ones are found. It is a 158708x2560 logical.
So I have:
[centers, radii] = imfindcircles(I,[15 35],'ObjectPolarity','bright','Sensitivity',0.91);
figure(1)
imshow(I)
viscircles(centers,radii);
and I want to save that output you see in the figure box (binary image with circles on it) to a image file. File format doesn't matter as long as it has the same resolution of 158708x2560 pixel.
Every suggestion I find online alters the resolution or makes the image broader, like when saving the figure directly you get a huge grey border and resolution goes down.
What would also work is a way to zoom into the figure but the zoom option in the figure menu does not magnify correctly. It does magnify but the image stays really thin so you can't see a thing.
Matrix: https://www.dropbox.com/s/rh9wakimc7atfhg/I.mat?dl=0
There are two round spots repeating. I want to find those, not the others. And export the image with the circles plotted over it.
Trying to segment out the lung region, I am having a lot of trouble. Incoming image is like this: (This is essentially a jpg conversion, and each pixel is 8 bits.)
I = dicomread('000019.dcm');
I8 = uint8(I / 256);
B = im2bw(I8, 0.007);
segmented = imclearborder(B);
Above script generates:
Q-1
I am interested in entire inner black part with white matter as well. I have started matlab couple of days ago, so not quite getting how can I do it. If it is not clear to you what kind of output I want, let me know-I will upload an image. But I think there is no need.
Q-2
in B = im2bw(I8, 0.007); why I need to give a threshold so low? with higher thresholds everything is white or black. I have read the documentation and as I understand it, the pixels with value less than 0.007 are marked black and everything above is white. Is it because of my 16-to-8 bit conversion?
An other automatic solution that I did quickly using ImageJ (there are the same algorithms in MatLab):
Automatic thresholding using Huang or Li in the color space of your choice (all of them work)
Opening with a structuring element of type disk (delete the small components)
Connected components labeling.
Delete the components that touches the border of the images.
Fill holes.
And you have a clean result.
Here's a working solution in python using OpenCV:
import cv2 #openCV
import numpy as np
filename = 'zFrkx.jpg' #name of file in quotations here... assumes file is in same dir as .py file
img_gray = cv2.imread(filename, 0) #converts jpg image to grayscale representation
min_val = 100 #try shifting these around to expand or collapse area of interest
max_val = 150
ret, lung_mask = cv2.threshold(img_gray, min_val, max_val, cv2.THRESH_BINARY_INV) #fixed threshold uses values you'll def above
lung_layer = cv2.bitwise_and(img_gray, img_gray, mask = lung_mask)
cv2.imwrite('cake.tif', lung_layer) #outputs desired layer to current working dir
I tried running the script with threshold values set arbitrarily to 100,150 and got the following result, from which you could select the largest continuous element using dilation and segmentation techniques (http://docs.opencv.org/master/d3/db4/tutorial_py_watershed.html#gsc.tab=0).
Also, I suggest you crop the bottom and top X pixels to cut out text since no lung will fill the top or bottom of the picture.
Use tif instead of jpg format to avoid compression related artifact.
I know you noted that you'd like the medullar(?) white matter, too. Would be glad to help with that, but could you first explain in plain english how your shared matlab code works? Seems to work pretty well for the WM.
Hope this helps!
What I have is a plot showing the area of connected components. What I want to do is to further work on the plot figure such as clean it up a bit or imcomplement it etc. and then be able to apply the axes from the original plot to this image and be able to extract the ylabel.
Let me explain the above issue with my code and some examples.
This is the plot I have, the y-axis represents the object areas. This is the important axis that I want to transfer to the new image.
Since I am interested in the axes only I copy that using
h = findobj(gcf,'type','axes');
So that I can work with the figure without the axes and borders interfering I save it without these attributes
set(gca, 'visible', 'off'); % Hide the axis and borders
hgexport(gcf, 'plot1.jpg', hgexport('factorystyle'), 'Format', 'jpeg');
This is what I get:
So far so good.
Now comes the processing or in other words changing the plot to my needs.
plot_img = rgb2gray(imread('plot1.jpg'));
img_bw_plot = im2bw(plot_img, graythresh(plot_img));
[rows cols] = size(plot_img);
new = zeros(size(plot_img));
for i = 1: rows
for j = 1: cols
if (img_bw_plot(i,j) == 0)
new(i, 1:10) = 255;
end
end
end
f = figure;
imshow(new);
copyobj(h,f)
This produces a weird overlapped image where instead of copying only the axes, the entire image is copied on top of the new. The datacursormode also fails to work beyond the over lapping image.
First of all I'm a little bit confused that if you have the figure in the first place why aren't you extracting your data from it using something like:
lines=findobj(gca,'type','line');
y=zeros(1,length(lines));
for i=1:length(lines)
y(i)=get(lines(i),'ydata');
end
and there you'll have all the data.
But let's say the original figure isn't like a figure figure where you'd have access to the children of the axes object (though all of them being copied together kind of suggests that this is not the case). What you need to realize is that an "axes" object in MATLAB isn't just the axes of the graph, but the whole graph. For example when you have 5 subplots, each of those smaller plots is an axes object and the graph itself is one of its children which is a "line" object (refer to my example above).
So after this long lecture :), one solution is that you could manually create those axes around your newly drawn image instead of copying the axes object as such:
set(gca,'visible','on');
s=size(new);
set(gca,'ytick',linspace(1,s(1),7),'yticklabel',linspace(6000,0,7));
This should do the trick of placing 7 ticks on the y-axis in the same manner as you have on your original figure. The same method would apply to manually creating the labels for the x-axis.
(I tried putting the resulting image here but I don't have the enough reputations to do so. That's on stackoverflow bro!)
Mind you, though, that this creates the labels on the graph giving you the illusion of the same axis while the actual coordinates of the points are actually determined by the size of the image you're saving. So if you want to make sure the image is the same size, you need to work on resizing your original figure to end up being the same size, which given then 0-6000, would be a really big image.
I finally solved my problem here with lennon310.
I have a picture of thousands of thin peaks in Time-Frequency picture.
I cannot see all the same time in one picture.
Depending on the physical width of my time window, some windows appear and some come visible.
Pictures of my data which I plot by imagesc
All pictures are from the same data points T, F, B.
How can you plot all peaks at once in a picture in Matlab?
You need to resize the image using resampling to prevent the aliasing effect (that craigim described as unavoidable).
For example, the MATLAB imresize function can perform anti-aliasing. Don't use the "nearest" resize method, that's what you have now.
Extension to #BenVoigt's answer
My try
B = abs(B);
F1 = filter2(B,T); % you need a different filter kernel because resolution is lower
T = filter2(B,F);
F = F1;
image1 = imagesc(B);
display1 = imresize(image1, [600 600], 'bilinear');
imshow(T*t, F*fs, display1);
where are some problems.
I get again picture where the resolution is not enough
2nd Extension to BenVoigt's answer
My suggestion for one kernel filter is with convolution of relative random error
data(find(data ~= 0)) = sin(pi .* data(find(data ~= 0))) ./ (pi*data(find(data ~= 0)));
data(find(data == 0)) = 1; % removing lastly the discontinuity
data1 = data + 0.0000001 * mean(abs(data(:))) * randn(size(data));
data = conv(data, data1);
Is this what BenVoigt means by the kernel filter for distribution?
This gives results like
where the resolution is still a problem.
The central peaks tend to multiply easily if I resize the window.
I had old code active in the above picture but it did not change the result.
The above code is still not enough for the kernel filter of the display.
Probably, some filter functions has to be applied to the time and frequency axis separately still, something like:
F1 = filter2(B,T); % you need a different kernel filter because resolution is lower
T = filter2(B,F);
F = F1;
These filters mess up the values on the both axis.
I need to understand them better to fix this.
But first to understand if they are the right way to go.
The figure has be resized still.
The size of the data was 5001x1 double and those of F and T 13635x1 double.
So I think I should resize lastly after setting axis, labels and title by
imresize(image, [13635 13635], 'bilinear');
since the distirbution is bilinear.
3rd Extension to BenVoigt's answer
I plot the picture now by
imagesc([0 numel(T)], [0 numel(F)], B);
I have a big aliasing problem in my pictures.
Probably, something like this should be to manipulate the Time-Frequency Representation
T = filter2(B,t); % you need a different filter kernel because resolution is lower
F = filter2(B,fs);
4th extension to BenVoigt's answer and comment
I remove the filters and addition of random relative errors.
I set the size of T,F,B, and run
imagesc([0 numel(T)], [0 numel(F)], B, [0 numel(B)])
I get still with significant aliasing but different picture
This isn't being flippant, but I think the only way is to get a wider monitor with a higher pixel density. MATLAB and your video card can only show so many pixels on the screen, and must decide which to show and which to leave out. The data is still there, it just isn't getting displayed. Since you have a lot of very narrow lines, some of them are going to get skipped when decisions are made as to which pixels to light up. Changing the time window size changes which lines get aliased away and which ones get lucky enough to show up on the screen.
My suggestions are, in no particular order: convolute your lines with a Gaussian along the time axis to broaden them, thus increasing the likelihood that part of the peak will appear on the screen. Print them out on a 600 dpi printer and see if they appear. Make several plots, each zooming in on a separate time window.
3rd Extension to BenVoigt's answer
My attempt to tell imagesc how big to make the image, instead of letting to use the monitor size, so it won't away data.
imagesc(T*t, F*fs, B, [50 50]);
% imagesc(T*t, F*fs, B');
% This must be last
imresize(image, 'bilinear', [64 30],);
I am not sure where I should specify the amount of pixels in x-axis and y-axis, either in the command imagesc or imresize.
What is the correct way of resizing the image here?